FPGA'de altera OpenCL SDK kullanılarak geliştirilen yoğun optik akış uygulaması

FPGA acceleration of compute-intensive algorithms is usually not regarded feasible because of the long Verilog or VHDL RTL design efforts they require. Data-parallel algorithms have an alternative platform for acceleration, namely, GPU. Two languages are widely used for GPU programming, CUDA and OpenCL. OpenCL is the choice of many coders due to its portability to most multi-core CPUs and most GPUs. OpenCL SDK for FPGAs and High-Level Synthesis (HLS) in general make FPGA acceleration truly feasible. In data-parallel applications, OpenCL based synthesis is preferred over traditional HLS as it can be seamlessly targeted to both GPUs and FPGAs. This thesis shares our experiences in targeting a demanding optical flow algorithm to a high-end FPGA as well as a high-end GPU using OpenCL. Throughput and power consumption results on both platforms are presented.